New shape clustering method based on contour DFT descriptor and modified SOFM neural network

A contour shape descriptor based on discrete Fourier transform (DFT) and a K-means algorithm modified self-organizing feature map (SOFM) neural network are established for shape clustering. The given shape is first sampled uniformly in the polar coordinate. Then the discrete series is transformed to frequency domain and constructed to a shape characteristics vector. Firstly, sample set is roughly clustered using SOFM neural network to reduce the scale of samples. K-means algorithm is then applied to improve the performance of SOFM neural network and process the accurate clustering. K-means algorithm also increases the controllability of the clustering. The K-means algorithm modified SOFM neural network is used to cluster the shape characteristics vectors which is previously constructed. With leaf shapes as an example, the simulation results show that this method is effective to cluster the contour shapes.